From ea2bba87149021e2f3ab441442d6a20c87e5bd70 Mon Sep 17 00:00:00 2001
From: "Olivier J.N. Bertrand" <olivier.bertrand@uni-bielefeld.de>
Date: Sat, 12 Oct 2019 22:34:33 +0200
Subject: [PATCH] Update doc
---
doc/source/index.rst | 2 +-
doc/source/tests/index.rst | 2 +-
doc/source/tests/moving.rst | 14 -
doc/source/tutorials/database.rst | 33 -
doc/source/tutorials/opticFlow_tutorial.ipynb | 625 ------------------
doc/source/tutorials/renderimage.rst | 25 -
6 files changed, 2 insertions(+), 699 deletions(-)
delete mode 100644 doc/source/tests/moving.rst
delete mode 100644 doc/source/tutorials/database.rst
delete mode 100644 doc/source/tutorials/opticFlow_tutorial.ipynb
delete mode 100644 doc/source/tutorials/renderimage.rst
diff --git a/doc/source/index.rst b/doc/source/index.rst
index e921c8a..b720946 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -26,7 +26,7 @@ Content
tutorials/index
references/index
tests/index
-
+
Indices and tables
==================
diff --git a/doc/source/tests/index.rst b/doc/source/tests/index.rst
index c27dd65..c386089 100644
--- a/doc/source/tests/index.rst
+++ b/doc/source/tests/index.rst
@@ -9,6 +9,6 @@ Tests
sensors
processing
comparing
- moving
database
maths
+ arenatools
diff --git a/doc/source/tests/moving.rst b/doc/source/tests/moving.rst
deleted file mode 100644
index 7edeb2d..0000000
--- a/doc/source/tests/moving.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-Moving
-======
-
-Agents
-~~~~~~
-
-.. automodule:: navipy.moving.test_agent
- :members:
-
-Maths
-~~~~~
-
-.. automodule:: navipy.moving.test_maths
- :members:
diff --git a/doc/source/tutorials/database.rst b/doc/source/tutorials/database.rst
deleted file mode 100644
index 55a2059..0000000
--- a/doc/source/tutorials/database.rst
+++ /dev/null
@@ -1,33 +0,0 @@
-How to generate a database using blender
-----------------------------------------
-.. literalinclude:: examples/blenddemo_beesampling.py
- :lines: 7, 8
-
-First we configure the rendering module
-
-.. literalinclude:: examples/blenddemo_beesampling.py
- :lines: 11, 12
-
-With the toolbox at disposition we just need to configure the \
-BeeSampling to render images on a regular 3D grid.
-
-.. literalinclude:: examples/blenddemo_beesampling.py
- :lines: 13,16-23
-
-If we want to use the distance to objects, we need to tell the \
-BeeSampling what is the maximum distance to objects in the environment.\
- Otherwise the distance can go until infinity, and since the image are \
-compressed in the database, all distances to object will be equal to \
-zero:
-
-.. literalinclude:: examples/blenddemo_beesampling.py
- :lines: 27-28
-
-Finally we can generate the database.
-
-.. literalinclude:: examples/blenddemo_beesampling.py
- :dedent: 4
- :lines: 31-32
-
-(:download:`Source code <examples/blenddemo_beesampling.py>`)
-(:download:`Blender world <../../../navipy/resources/forest_world.blend>`)
diff --git a/doc/source/tutorials/opticFlow_tutorial.ipynb b/doc/source/tutorials/opticFlow_tutorial.ipynb
deleted file mode 100644
index 5cab175..0000000
--- a/doc/source/tutorials/opticFlow_tutorial.ipynb
+++ /dev/null
@@ -1,625 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Using the Optic Flow function\n",
- "\n",
- "This Tutorial explains how to use the optic flow function of the navipy toolbox.\n",
- "\n",
- "The function computes the optic for a positions with an already rendered scene.\n",
- "Therefore if the optic flow of a whole trajectory is wanted, this function has to be called in a loop for each step.\n",
- "\n",
- "So to get the optic flow we need:\n",
- "* the viewing direction (in radians)\n",
- "* the already rendered scene\n",
- "* velocity of the bee including the accaleration\n",
- "\n",
- "The constructor of the optic_flow function is the following:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'optic_flow' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m<ipython-input-1-b9f43b3f8149>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0moptic_flow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscene\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mviewing_directions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvelocity\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdistance_channel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[1;31mNameError\u001b[0m: name 'optic_flow' is not defined"
- ]
- }
- ],
- "source": [
- "optic_flow(scene, viewing_directions, velocity, distance_channel=3)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* scene: ibpc\n",
- "* param viewing_directions: viewing direction of each pixel\n",
- " (azimuth,elevation) in radians\n",
- "* param velocity: pandas series\n",
- " (x,y,z,alpha,beta,gamma,dx,dy,dz,dalpha,dbeta,dgamma)\n",
- "* distance channel: distance; possible choices= 0,1,2,3; default :3"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The scenes must be of h x w 4 x 1 where h is the hight of image and w the width. The third dimension is needed for the different channels. We have R, G, B, D. Where R is the red, G is the green, B is the blue and D is the distance channel. Here is an example for how to initialize a random scene with 180 x 180 dimension. \n",
- "\n",
- "\n",
- "For an example on how to get a rendered scene please refer to the other tutorials."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "scene = np.random.random((180, 360, 4, 1))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To set up the viewing direction with a 180 x 180 scene, the following can be done:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "elevation = np.arange(-np.pi/2, np.pi/2, 2*(np.pi/360))\n",
- "azimuth = np.arange(0, 2*np.pi, 2*(np.pi/360))\n",
- "\n",
- "viewing_directions = np.zeros((180, 2))\n",
- "viewing_directions[:, 0] = elevation\n",
- "viewing_directions[:, 1] = azimuth[0:180]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The velocity contains the velocity of the current position as well as the acceleration (difference of velocity to the old position). The velocity must be a pandas dataframe with a multiindex, where the angles index must contain the convention to be used.\n",
- "The conventions that can be used are:\n",
- "'sxyz', 'sxyx', 'sxzy','sxzx', 'syzx', 'syzy',\n",
- "'syxz', 'syxy', 'szxy', 'szxz', 'szyx', 'szyz',\n",
- "'rzyx', 'rxyx', 'ryzx', 'rxzx', 'rxzy', 'ryzy',\n",
- "'rzxy', 'ryxy', 'ryxz', 'rzxz', 'rxyz', 'rzyz'\n",
- "\n",
- "Here is an example on how to initilize it. In this example the bee is rotating around the x axis (yaw) and stays constant otherwise."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "\n",
- "# only the yaw is changes aka the bee is tilted up\n",
- "positions = np.array([[-20, -20, 2.6, 1.57079633, 0, 0],\n",
- " [-20, -20, 2.6, 1.90079633, 0, 0]])\n",
- "x = positions[0, 0]\n",
- "y = positions[0, 1]\n",
- "z = positions[0, 2]\n",
- "yaw = positions[0, 3]\n",
- "pitch = positions[0, 4]\n",
- "roll = positions[0, 5]\n",
- "\n",
- "dx = positions[1, 0]-positions[0, 0]\n",
- "dy = positions[1, 1]-positions[0, 1]\n",
- "dz = positions[1, 2]-positions[0, 2]\n",
- "dyaw = positions[1, 3]-positions[0, 3]\n",
- "dpitch = positions[1, 4]-positions[0, 4]\n",
- "droll = positions[1, 5]-positions[0, 5]\n",
- "\n",
- "tuples = [('location', 'x'), ('location', 'y'),\n",
- " ('location', 'z'), ('location', 'dx'),\n",
- " ('location', 'dy'), ('location', 'dz'),\n",
- " ('rxyz', 'alpha_0'), ('rxyz', 'alpha_1'),\n",
- " ('rxyz', 'alpha_2'), ('rxyz', 'dalpha_0'),\n",
- " ('rxyz', 'dalpha_1'), ('rxyz', 'dalpha_2')]\n",
- "index = pd.MultiIndex.from_tuples(tuples,\n",
- " names=['position', 'orientation'])\n",
- "velocity = pd.Series(index=index)\n",
- "\n",
- "velocity['location']['x'] = x\n",
- "velocity['location']['y'] = y\n",
- "velocity['location']['z'] = z\n",
- "velocity['rxyz']['alpha_0'] = yaw\n",
- "velocity['rxyz']['alpha_1'] = pitch\n",
- "velocity['rxyz']['alpha_2'] = roll\n",
- "velocity['location']['dx'] = dx\n",
- "velocity['location']['dy'] = dy\n",
- "velocity['location']['dz'] = dz\n",
- "velocity['rxyz']['dalpha_0'] = dyaw\n",
- "velocity['rxyz']['dalpha_1'] = dpitch\n",
- "velocity['rxyz']['dalpha_2'] = droll"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "So then the optic flow function can be called as follows:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from navipy.processing import mcode\n",
- "\n",
- "rof, hof, vof = mcode.optic_flow(scene, viewing_directions,\n",
- " velocity)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here is an example that loads a scene from the database and calculates the horizontal, vertical and rotational optic flow"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.image.AxesImage at 0x7fa2a919fe48>"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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AtwG4b7x+H4BfTY/2Hbrt9AeY7rRrn4Era4C40/cCIxIHWZm+jX5CRSRSfafuw8heYGXO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\nhzHPeEIrEm1+/aPU8m1bsm0rEu252T96OoiEELI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- "text/plain": [
- "<matplotlib.figure.Figure at 0x7fa3001030f0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Load the necessary modules\n",
- "from navipy.processing import mcode\n",
- "from navipy.database import DataBaseLoad\n",
- "from matplotlib.image import imsave\n",
- "import numpy as np\n",
- "import os\n",
- "import matplotlib.pyplot as plt\n",
- "# Load the database, and specify the\n",
- "# the output directory to save the list\n",
- "# of images\n",
- "import pkg_resources\n",
- "%matplotlib inline\n",
- "database = pkg_resources.resource_filename(\n",
- " 'navipy',\n",
- " 'resources/database.db')\n",
- "mydb = DataBaseLoad(database)\n",
- "\n",
- "my_scene = mydb.scene(rowid=2)\n",
- "rof, hof, vof = mcode.optic_flow(my_scene, viewing_directions,\n",
- " velocity)\n",
- "f, axarr = plt.subplots(2, 2, figsize=(15, 4))\n",
- "\n",
- "plt.subplot(221)\n",
- "to_plot_im = my_scene[:, :, :3, 0].astype(float)\n",
- "to_plot_im = my_scene[:, :, :3, 0]\n",
- "to_plot_im -= to_plot_im.min()\n",
- "to_plot_im /= to_plot_im.max()\n",
- "to_plot_im = to_plot_im * 255\n",
- " \n",
- "to_plot_im = to_plot_im.astype(np.uint8)\n",
- " \n",
- "to_plot_dist = my_scene[:, :, 3, 0]\n",
- "plt.imshow(to_plot_im)\n",
- "\n",
- "plt.subplot(222)\n",
- "plt.imshow(hof, cmap='gray')\n",
- "plt.subplot(223)\n",
- "plt.imshow(vof, cmap='gray')\n",
- "plt.subplot(224)\n",
- "plt.imshow(rof, cmap='gray')\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": true
- },
- "source": [
- "# Example with a trajectory\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dict_keys(['__globals__', '__version__', 'trajectory', 'cylinders', 'nests', '__header__'])\n",
- "(7661, 4)\n",
- "conv rzyx\n"
- ]
- },
- {
- "data": {
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- " }\n",
- "\n",
- " .dataframe thead tr th {\n",
- " text-align: left;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th></th>\n",
- " <th colspan=\"3\" halign=\"left\">location</th>\n",
- " <th colspan=\"3\" halign=\"left\">rzyx</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th></th>\n",
- " <th>x</th>\n",
- " <th>y</th>\n",
- " <th>z</th>\n",
- " <th>alpha_0</th>\n",
- " <th>alpha_1</th>\n",
- " <th>alpha_2</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>3.056519</td>\n",
- " <td>-214.990482</td>\n",
- " <td>9.330593</td>\n",
- " <td>2.79751</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>4.611665</td>\n",
- " <td>-215.020314</td>\n",
- " <td>8.424138</td>\n",
- " <td>2.80863</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4.556650</td>\n",
- " <td>-214.593236</td>\n",
- " <td>9.185016</td>\n",
- " <td>2.81407</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4.643091</td>\n",
- " <td>-213.829769</td>\n",
- " <td>10.542035</td>\n",
- " <td>2.82704</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>4.647302</td>\n",
- " <td>-214.431592</td>\n",
- " <td>7.461187</td>\n",
- " <td>2.82896</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " location rzyx \n",
- " x y z alpha_0 alpha_1 alpha_2\n",
- "0 3.056519 -214.990482 9.330593 2.79751 0 0\n",
- "1 4.611665 -215.020314 8.424138 2.80863 0 0\n",
- "2 4.556650 -214.593236 9.185016 2.81407 0 0\n",
- "3 4.643091 -213.829769 10.542035 2.82704 0 0\n",
- "4 4.647302 -214.431592 7.461187 2.82896 0 0"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from scipy.io import loadmat\n",
- "import numpy as np\n",
- "import os\n",
- "from navipy.trajectories import Trajectory\n",
- "import pkg_resources\n",
- "# Use the trafile from the resources\n",
- "# You can adapt this code, by changing trajfile \n",
- "# with your own trajectory file\n",
- "trajfile = pkg_resources.resource_filename(\n",
- " 'navipy',\n",
- " 'resources/sample_experiment/Lobecke_JEB_2018/Y101_OBFlight_0001.mat')\n",
- "csvtrajfile, _ = os.path.splitext(trajfile)\n",
- "csvtrajfile = csvtrajfile+'.csv'\n",
- "mymat = loadmat(trajfile)\n",
- "# matlab files are loaded in a dictionary\n",
- "# we need to identify, under which key the trajectory has been saved\n",
- "print(mymat.keys())\n",
- "key = 'trajectory'\n",
- "# Arrays are placed in a tupple. We need to access the level\n",
- "# of the array itself.\n",
- "mymat = mymat[key][0][0][0]\n",
- "# The array should be a numpy array, and therefore as the .shape function\n",
- "# to display the array size:\n",
- "print(mymat.shape)\n",
- "# In this example the array has 7661 rows and 4 columns\n",
- "# the columns are: x,y,z, yaw. Therefore pitch and roll were assumed to be constant (here null)\n",
- "# We can therefore init a trajectory with the convention for rotation\n",
- "# often yaw-pitch-roll (i.e. 'rzyx') and with indeces the number of sampling points we have \n",
- "# in the trajectory\n",
- "rotconvention = 'rzyx'\n",
- "indeces = np.arange(0,mymat.shape[0])\n",
- "mytraj = Trajectory(rotconv = rotconvention, indeces=indeces)\n",
- "# We now can assign the values\n",
- "mytraj.x = mymat[:,0]\n",
- "mytraj.y = mymat[:,1]\n",
- "mytraj.z = mymat[:,2]\n",
- "mytraj.alpha_0 = mymat[:,3]\n",
- "mytraj.alpha_1 = 0\n",
- "mytraj.alpha_2 = 0\n",
- "# We can then save mytraj as csv file for example\n",
- "mytraj.to_csv(csvtrajfile)\n",
- "mytraj.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Then we need the velocity to calculate the optic flow"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
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- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1.555146</td>\n",
- " <td>-0.029831</td>\n",
- " <td>-0.906455</td>\n",
- " <td>0.01112</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>-0.055015</td>\n",
- " <td>0.427078</td>\n",
- " <td>0.760878</td>\n",
- " <td>0.00544</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>0.086441</td>\n",
- " <td>0.763467</td>\n",
- " <td>1.357019</td>\n",
- " <td>0.01297</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>0.004211</td>\n",
- " <td>-0.601823</td>\n",
- " <td>-3.080847</td>\n",
- " <td>0.00192</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " dx dy dz dalpha_0 dalpha_1 dalpha_2\n",
- "0 NaN NaN NaN NaN NaN NaN\n",
- "1 1.555146 -0.029831 -0.906455 0.01112 0.0 0.0\n",
- "2 -0.055015 0.427078 0.760878 0.00544 0.0 0.0\n",
- "3 0.086441 0.763467 1.357019 0.01297 0.0 0.0\n",
- "4 0.004211 -0.601823 -3.080847 0.00192 0.0 0.0"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "vel = mytraj.velocity()\n",
- "vel.head()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Iterate through the trajectory to get the optic flow of each scene"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "tuples = [('location', 'x'), ('location', 'y'),\n",
- " ('location', 'z'), ('location', 'dx'),\n",
- " ('location', 'dy'), ('location', 'dz'),\n",
- " ('rxyz', 'alpha_0'), ('rxyz', 'alpha_1'),\n",
- " ('rxyz', 'alpha_2'), ('rxyz', 'dalpha_0'),\n",
- " ('rxyz', 'dalpha_1'), ('rxyz', 'dalpha_2')]\n",
- "index = pd.MultiIndex.from_tuples(tuples,\n",
- " names=['position', 'orientation'])\n",
- "\n",
- "for i in range(1,10): \n",
- " velocity = pd.Series(index=index)\n",
- " velocity['location']['x'] = mytraj.x[i]\n",
- " velocity['location']['y'] = mytraj.y[i]\n",
- " velocity['location']['z'] = mytraj.z[i]\n",
- " velocity['rxyz']['alpha_0'] = mytraj.alpha_0[i]\n",
- " velocity['rxyz']['alpha_1'] = mytraj.alpha_1[i]\n",
- " velocity['rxyz']['alpha_2'] = mytraj.alpha_2[i]\n",
- " velocity['location']['dx'] = vel.dx[i]\n",
- " velocity['location']['dy'] = vel.dy[i]\n",
- " velocity['location']['dz'] = vel.dz[i]\n",
- " velocity['rxyz']['dalpha_0'] = vel.dalpha_0[i]\n",
- " velocity['rxyz']['dalpha_1'] = vel.dalpha_1[i]\n",
- " velocity['rxyz']['dalpha_2'] = vel.dalpha_2[i]\n",
- " \n",
- " my_scene = mydb.scene(rowid=i)\n",
- " rof, hof, vof = mcode.optic_flow(my_scene, viewing_directions,\n",
- " velocity)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "kernelspec": {
- "display_name": "Python [Root]",
- "language": "python",
- "name": "Python [Root]"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.5.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/doc/source/tutorials/renderimage.rst b/doc/source/tutorials/renderimage.rst
deleted file mode 100644
index 7688636..0000000
--- a/doc/source/tutorials/renderimage.rst
+++ /dev/null
@@ -1,25 +0,0 @@
-Render a single image
----------------------
-.. literalinclude:: examples/blenddemo_cyberbee.py
- :lines: 5
-
-With the toolbox at disposition we just need to configure the \
-Cyberbee to render images at desired positions.
-
-.. literalinclude:: examples/blenddemo_cyberbee.py
- :lines: 8-13
-
-To render a scene at a given positions, we assign position orientation \
-to and then tell the cyberbee to get the scene at that point.
-
-.. literalinclude:: examples/blenddemo_cyberbee.py
- :lines: 16-24
-
-(:download:`Source code <examples/blenddemo_cyberbee.py>`)
-(:download:`Blender world <../../../navipy/resources/forest_world.blend>`)
-
-
-
-Create a movie
---------------
-
--
GitLab